Accurate medical diagnosis often depends on the correct display of diagnostically relevant regions in images. ith the recent advance of computed radiographic systems and digital radiographic systems, the acquisition of an image and its final ‘look’ are separated. His provides flexibility to users, but also introduces the difficulty in setting an appropriate tone scale for image display. n optimal tone scale, in general, is dependent upon the examination type, the exposure conditions, the image acquisition device and the choice of output devices as well as the preferences of the radiologist. among them, the examination type is one determinant factor, since it is directly related to the characteristics of signal and clinical important parts in images. Therefore, the success of classifying examination types can greatly benefit the optimal rendition of images. Another emerging field of using the examination type classification is digital Picture Archiving and Communication Systems (PACS). To date, most radiograph related information is primarily based on manual input. This step is often skipped or the incorrect information is recorded in the image header, which hinders the efficient use of images in routine medical practice and patient care. The automated image classification has potential to solve the above problem by organizing and retrieving images based on image contents. This can make the medical image management system more rational and efficient, and undoubtedly improve the performance of PACS.
However, it is difficult to let a computer automatically and efficiently analyze contents in images and classify images, since image data is more structurally complex than other kinds of data and the way by which human beings capture the image contents, group image features into meaningful objects and attach semantic descriptions to images through model matching has not been fully understood to automate the analysis procedure. Furthermore, segmenting an image into regions corresponding to individual objects, extracting features from the image that capture the perceptual and semantic meanings, and matching the image with the proposed model based on extracted features also make the analysis problem more challenging.
Various systems have been proposed in the recent literatures for content-based image classification and retrieval, such as QBIC (W. Niblack, et al, “The QBIC project: Querying images by content using color, texture, and shape” Proc. SPIE Storage and Retrieval for Image and Video Databases, Feburary 1994), Photobook (A. Pentland, et. al. “Photobook: Content-based manipulation of image database”. International Journal of Computer Vision, 1996), Virage (J. R. Bach, et al. “The Virage image search engine: An open framework for image management” Proc. SPIE Storage and Retrieval for image and Video Database, vol 2670, pp. 76-97, 1996), Visualseek (R. Smith, et al. “Visualseek: A fully automated content-based image query system” Proc ACM Multimedia 96, 1996), Netra (Ma, et al. “Netra: A toolbox for navigating large image databases” Proc IEEE Int. Conf. On Image Proc. 1997), and MAR (T. S. Huang, et. al, “Multimedia analysis and retrieval system (MARS) project” Proc of 33rd Annual Clinic on Library Application of Data Processing Digital Image Access and Retrieval, 1996). These systems follow the same paradigm which treats an image as a whole entity and represents it via a set of low-level feature attributes, such as color, texture, shape and layout. As a result, these feature attributes together form a feature vector for an image. The image classification is based on clustering these low-level visual feature vectors. Such clustering-based classification schemes are usually time-consuming and of limited practical use since little of the image object semantics is explicitly modeled. Another problem is that these systems use images collected from the world wide web. Usually, the most effective feature is color. Unfortunately, the color-based features are not available in most medical images.
I. Kawshita et. al. (“Development of Computerized Method for Automated Classification of Body Parts in Digital Radiographs”, RSNA 2002) present a method to classify six body parts. The method examines the similarity of a given image with a set of template images by using the cross-correlation values as the similarity measures. However, the manual generation of the template images is quite time consuming, and more crucial, it is highly observer dependent, which may introduce error in the classification. J. Dahmen, et al (“Classification of Radiographs in the ‘Image Retrieval in Medical Application’-System”, Procs 6th International RIAO Conference on Content-Based Multimedia Information Access, Paris, France, 2000; 551-566) teach a method to classify radiographs by using a new distortion model and an extended version of simard's tangent distance with a kernel density based classifier. Both of the above methods suffer problems in handling rotation and translation variance of anatomy in radiographs. So the result measures cannot accurately represent the features in radiographs. In addition, no preprocessing is implemented in the above methods. For example, the unexposed regions caused by the blocking of the x-ray collimator during the exposure may result in a significant white borders surrounding the image. If such regions are not removed in a pre-processing step and therefore used in the computation of similarity measures, the classification results can be seriously biased. Luo et. al (“Knowledge—based Image Understanding and Classification System for Medical Image Databases”, Proceedings of SPIE—the International Society for Optical Engineering. Vol. 4694, No. 22, February 2002. pp. 1224-1234) disclose a method for classification using shape information and model match. The method employs the edge direction histogram to describe the global shape of anatomy, and classifies images based on six scale, rotation and translation features extracted from their edge direction histograms. However, the extracted features are not sufficient to fully represent the characteristics of the edge direction histogram. As a result, the classifier's performance is hard to be improved.
Given the drawbacks and limitation of the prior art, there exists a need for a method to automatically classify radiographic images.